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Advanced Visual Interpretation

🎯 Purpose

This guide provides advanced visual tools for evaluating clustering models. It focuses on diagnosing cohesion, separation, density structure, and interpretability. It supports model comparison, hyperparameter tuning, and communication in unsupervised workflows.

🧭 1. Dimensionality Reduction Visualization (PCA / UMAP / t-SNE)

Purpose: Visually inspect cluster shape, separation, and noise.

import umap
embedding = umap.UMAP(n_neighbors=15).fit_transform(X_scaled)
plt.scatter(embedding[:, 0], embedding[:, 1], c=labels, cmap="Spectral")

✔️ Use consistent color palette across models ✔️ Apply same reduction method for fair comparisons

📈 2. Silhouette Plot (Cohesion vs Separation)

Purpose: Assess how well each sample fits within its cluster.

from sklearn.metrics import silhouette_samples
samples = silhouette_samples(X_scaled, labels)

✔️ Taller bars = stronger assignment ⚠️ Negative values = potential misclassification

📉 3. Cluster Size Distribution

Purpose: Detect cluster imbalance or dominance.

pd.Series(labels).value_counts().plot(kind='bar')

✔️ Expect noise label (-1) for DBSCAN / HDBSCAN ✔️ Balance helps interpretability and fairness

🌡 4. Density and Distance Heatmaps

Purpose: Visualize pairwise distance structure.

from sklearn.metrics import pairwise_distances
sns.heatmap(pairwise_distances(X_scaled))

✔️ Use with hierarchical methods or to explain DBSCAN structure

🧮 5. Centroid / Medoid Profiles (Radar / Heatmaps)

Purpose: Explain group characteristics by feature.

grouped = df.groupby('cluster').mean()
sns.heatmap(grouped.T, cmap="coolwarm")

✔️ Ideal for stakeholder presentations ✔️ Use parallel coordinates for wide feature spaces

📦 6. Silhouette Score vs Hyperparameter Plot

Purpose: Tune k, eps, or model choice.

plt.plot(k_values, silhouette_scores)

✔️ Visualizes clustering quality over parameter sweep ✔️ Elbow, knee, or peak = strong candidate

🔁 7. Cluster Overlap or Drift Comparison

Purpose: Compare clusters over time, feature changes, or scale.

  • Compare UMAP/PCA projections by model run
  • Use color-coded label overlay
  • Use Adjusted Rand Index or Confusion Matrix between runs

📋 Analyst Visual QA Checklist

  • [ ] PCA/UMAP shows clear grouping?
  • [ ] Silhouette plot reviewed?
  • [ ] Cluster size balance visualized?
  • [ ] Centroid heatmaps prepared?
  • [ ] Parameter sweep curve used to justify k/eps?
  • [ ] Drift or rerun visuals saved?

💡 Final Tip

“If your clustering visuals don’t tell a story, neither will your segments. Use visual cohesion to validate structural insight.”

Use with: Clustering Statistical Summary, Evaluation Checklist, and Decision Card.